韧性视角下城轨交通关键车站识别与恢复顺序优化OA

To address operational disruption risks in urban rail transit networks arising from station fail-ures,this study proposes a systematic resilience optimization methodology based on a weighted coupled map lattice(CML)model integrated with an improved simulated annealing algorithm.First,by comprehensively incorporating station degree,inter-station passenger flows,and boarding/alight-ing volumes,a weighted CML model is constructed to accurately simulate cascading failure processes following station disruptions.Structural and service performance metrics derived from this simulation are then employed to quantify system resilience.Subsequently,two optimization models are estab-lished:A critical station identification model aimed at minimizing cumulative resilience loss,and a re-covery sequence optimization model for failed stations designed to maximize restoration efficiency.To enhance algorithmic robustness and search efficiency,an improved simulated annealing algorithm fea-turing dynamic cooling rates and hybrid perturbation strategies is developed.Finally,the Wuhan Metro network serves as a case study for empirical validation.Results demonstrate that,compared to traditional single-indicator-based methods(such as those relying solely on station degree,inter-station flow,or boarding/alighting flow),the critical stations identified by the proposed model(primarily high-flow non-transfer stations)induce 7%to 13%greater network performance degradation upon failure.Moreover,the recovery sequence optimization model and algorithm yield a 3%to 7%improvement in restoration efficiency,validating the effectiveness of the proposed approach in enhancing network ro-bustness and recovery capacity.